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@INPROCEEDINGS{John:1029111,
author = {John, Chelsea Maria and Herten, Andreas and Kesselheim,
Stefan and Ruprecht, Daniel},
title = {{H}arnessing {F}ourier {N}eural {O}perator {F}or
{R}ayleigh–{B}énard {C}onvection},
school = {TUHH},
reportid = {FZJ-2024-04967},
year = {2024},
abstract = {Rayleigh-Bénard convection, is a classic fluid dynamics
problem, with applications in geophysical, astrophysical,
and industrial flows. Fourier Neural Operator (FNO)
leverages neural networks and Fourier analysis to
efficiently model spatiotemporal dynamics in fluid systems,
offering a promising avenue for accurate and scalable
simulations. In this poster, first results on the
application of FNO for tackling the Rayleigh-Benard
convection equations is presented.},
month = {May},
date = {2024-05-14},
organization = {PhysML Workshop 2024, Oslo (Norway),
14 May 2024 - 16 May 2024},
subtyp = {Other},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
and Research Groups (POF4-511) / 5121 - Supercomputing $\&$
Big Data Facilities (POF4-512) / Inno4Scale - Innovative
Algorithms for Applications on European Exascale
Supercomputers (101118139) / ATML-X-DEV - ATML Accelerating
Devices (ATML-X-DEV)},
pid = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5121 /
G:(EU-Grant)101118139 / G:(DE-Juel-1)ATML-X-DEV},
typ = {PUB:(DE-HGF)24},
doi = {10.34734/FZJ-2024-04967},
url = {https://juser.fz-juelich.de/record/1029111},
}